Author's note: This post was chosen as an Editor's Selection at ResearchBlogging.org. Thanks for the support!
Ovarian cancer is the fifth leading cause of cancer related mortality among women. Like many diseases, there is a stark difference in survival rates depending on detection times. When ovarian cancer is detected at stage I, there is a 90% 5 year survival rate. Compare that with the 33% 5 year survival rate when the ovarian cancer is detected in stage III and IV. This disease is unfortunately asymptomatic at early stages, drastically eliminating the odds of discovery with enough time to make a difference.
While using traditional diagnostics like imaging, biopsy, and genetic analysis is impractical for regular screening, there are alternative methods used for women who are high-risk for ovarian cancer or who have family history. Transvaginal sonography can be used annually although it has been shown to have limited efficacy. Blood serum can also be tested to indicate ovarian cancer, but this method only has a sensitivity of 72% at specificity of 95%. Sensitivity and specificity are used to measure how well a system can detect something. To calculate specificity in our case, imagine 100 women without ovarian cancer are tested, and only 5 women are incorrectly told that they have ovarian cancer. This would undoubtedly be corrected in a follow up test. But to calculate sensitivity, imagine 100 women with ovarian cancer and 28 women are incorrectly told that they do not have it.
Not only are these tests inconclusive, they are extremely invasive. In the case of transvaginal sonography, an instrument is inserted in the vagina to check the ovaries. With blood serum testing, blood obviously must be drawn. Biochips currently exist to detect ovarian cancer based on protein biomarkers or DNA sequences, but these rely on fluorescence or chemiluminescence and are designed to be used in laboratory settings. None of the previous methods lend themselves to be used in point-of-care (POC) settings. An ideal POC device would not require expensive parts, be usable by limited trained personnel or be too complex. This would allow it to be used in resource-rich and resource-limited settings, especially if it does not need a continuous power source.
Researchers from Harvard Medical School have developed a cell phone system to detect ovarian cancer that should address the lacking areas of diagnosis so far. “Integration of cell phone imaging with microchip ELISA to detect ovarian cancer HE4 biomarker in urine at the point-of-care” was featured in the 2011 issue 11 of Lab on a Chip. Utkan Demirci et al. demonstrate a method to non-invasively detect ovarian cancer efficiently with urine and a cell phone. At the heart of this system is an enzyme-linked immunosorbent assay (ELISA). ELISA is a very common technique used in protein detection. In this case, a sandwich ELISA is used to detect the ovarian cancer biomarker Human epididymis protein 4 (HE4). Antibodies targeted to HE4 are conjugated to horseradish peroxidase which catalyzes a substrate and causes blue color to develop. We should then be able to ascertain the amount of HE4 originally in solution by quantifying the resulting color. This process takes place in three different microfluidic channels on a microchip the size of a stamp. These three channels allow a sample to be treated in triplicate or for many samples to be tested at once.
Two methods were used to detect the change in color. The first method utilized a cell phone (more specifically Sony-Ericsson i790). This took advantage of the built in camera and processing power, allowing all processing steps to be carried out on the single device. The second method uses a lensless charge-coupled device (CCD). CCDs are found in digital cameras and have completely changed the way we capture images. In fact, the cell phone used has its own CCD inside. The CCD is used directly with a computer which analyzes the image with MATLAB. Both methods take a picture of the three microfluidic channels on the chip and compare the colors of the channels to previously measured standards.
Before this system can be tested on actual samples, it has to be calibrated with known samples. HE4 was evaluated from 1,250 to 19.5 ng/mL, which was its detection limit. I’m unsure how much urine is actually needed. Each sample was diluted twenty times, and each channel can only handle 96.75 µL including the ELISA solutions. In order to make sure that ELISA was occurring correctly on the microchip, the colored solution was transferred to a 96-well microplate and the optical density was measured with a spectrophotometer. This was validated and a strong correlation between HE4 concentration and color was found for the CCD and cell phone with high R2 values above 0.90. After this calibration, the system was used to differentiate between the urine samples of 19 women with ovarian cancer and 20 women without ovarian cancer. The standard microplate technique and the cell phone and CCD methods were able to distinguish between the normal and cancer samples with statistical significance. When operating at a specificity of 90%, the cell phone and CCD tests achieved 89.5% and 84.2% sensitivity respectively. These results indicate that the new methods can efficiently and effectively detect ovarian cancer in urine.
Wang, S., Zhao, X., Khimji, I., Akbas, R., Qiu, W., Edwards, D., Cramer, D., Ye, B., & Demirci, U. (2011). Integration of cell phone imaging with microchip ELISA to detect ovarian cancer HE4 biomarker in urine at the point-of-care Lab on a Chip, 11 (20) DOI: 10.1039/C1LC20479C